Network service providers and device manufacturers are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services. One of the network features that are recently gaining increasing attention is providing online services or content (e.g. applications, games, media, etc.) via an online store or repository. In order to increase the number of hits for suggested services, the online service providers need to suggest relevant services for every category of users based on various factors such as user interests (past experience), type and capabilities of user equipment, geographical location, etc. The factors involved in decision making may be global (e.g., applied to all user equipments), local (e.g., applied to certain groups of user equipment or at specific regions or locations) or individual (e.g., specifically set by the user of an equipment). Manual creation of all the possible combinations of these factors for producing a relevant front page of service suggestions for each group of users is a difficult and time consuming task. On the other hand if the task is completely automated, the required level of control to meet the needs of specific groups of users may not be achieved.